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W2017_PSYC212_Lecture 3

W2017_PSYC212_Lecture 3 - Lecture 3 Thursday January 12th...

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Lect2 set - modified slides after #27 PSYC212_2 017_lectu... Lecture3_P SY212_Wi... Modified again (DL Feb 1st) lecture2_ne westmodi... Slides have been adjusted Recap thresholding methods Hard to understand Signal detection theory Magnitude rating Recapitulation will be reiterated in other lectures Elements of neurophysiology TODAY - MSURJ submissions due Jan 20th, 2017 - Polling system works! - 25. Relationship between stim level and % someone reports is not a perfect staircase step, it's an S shape Function % respond present vs. stimulus level (arbitrary units) [start on slide 26] absolute threshold 26. Will go through more detail on each one Four different techniques representative of most techniques people use to measure thresholds Thresholding methods method of constant stimuli, method of limits, staircase method, method of adjustment 27. Announcements: Lecture 3 - Thursday, January 12th, 2017 January 12, 2017 1:30 PM PSYC 212 Page 1
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Arbitrary stim intensities to test Take a wide range, generally (want to make sure your threshold is within the range) Pre-select thresholds you want to test In example, repeated 10 times (10 cycles) Test in random fashion (each column) Method of constant stimuli 28. Even though supra threshold reported N, and sub threshold reported Y Due to some noise, fluctuations in our nervous system Zooming on cycle #4 Done in a random fashion (for ex started out with 0.4 --> 0.8 --> 0.3) Set it to be at 0.5 (artificial example) If no noise at the system, the participant would say YES if signal intensity is higher than threshold Normally don't know it Simulation, don't know real threshold 10 trials Noise has been randomly simulated to be -0.5 --> 0.5 (relatively high) Sensory nerves have baseline activity that are random (electrical impulses) which are added on top of the electrical impulses by the real stimulus Call it static activity in our nerves More on it in the neurophgy Noise = random fluctuation in our nervous systems When person makes decision it's on the combined signal + noise At 0.8 said No, and 0.3 said Yes (by chance those had high noise) The 2 trials with errors ( aberrant responses) Seeing if these techniques are accurate or not Pre-specify the threshold to see if we can uncover threshold despite a lot of noise These simulations: Method constant stimuli numerical example 29. X is real signal (got rid of the noise) - Normally don't know what the noise is - We know what is given to subject - Mathematical process *red is identified threshold, black is real threshold Note 2 weird trials at 0.3 and 0.8 The circles represent binary yes or no detection S-shape Fit the dots as best as it can When the threshold is really sharp, the function will have clear S shape We try to fit logistic function Method of constant stimuli figure for threshold detection curve 30.
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